Titlebook: Automating the Analysis of Spatial Grids; A Practical Guide to Valliappa Lakshmanan Book 2012 Springer Science+Business Media Dordrecht. 20

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Automated Analysis of Spatial Grids: Motivation and Challenges,ems (GIS) are software systems that are capable of storing and carrying out spatial operations – operations that make use of geographic coordinates – on spatial grids. However, a GIS is typically employed interactively. Remote sensing offers substantial benefits in observing the environment, but the

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Geospatial Grids,arth (such as to an ellipsoid) and results in distortion. A variety of map projections are available, and pointers are given on choosing the appropriate map projection to handle trade-offs in the type of distortion associated with each projection. To analyze multiple geospatial grids, it is necessar

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Global and Local Image Statistics, In this chapter, different definitions and methods to obtain distances are considered. These include distances between points, distances from a point to a cluster of points, and distances between two clusters. Fast Euclidean distance transforms are introduced, and considerations of when to choose o

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Identifying Objects,of pixels that together form a real-world entity such as a mountain range, city, or a thunderstorm. Commonly, objects are formed by combining high-valued pixels that are connected into objects. This is done using a process called region growing. Once candidate objects have been identified, it is pos

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D. K. Likhi,C. Sabita,Akanksha Rao encode every relationship. Another challenge with creating automated algorithms to analyze spatial grids is that low-level image processing operations are rarely sufficient, so domain knowledge needs to be incorporated. Hence, in order to create an automated algorithm to operate on geospatial data,